Surface modelling of global terrestrial ecosystems under three climate change scenarios

A high accuracy and speed method (HASM) of surface modelling is developed to find a solution for error problem and to improve computation speed. A digital elevation model (DEM) is established on spatial resolution of 13.5km×13.5km. Regression formulations among temperature, elevation and latitude ar...

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Main Authors: Yue, Tian-Xiang, Fan, Ze-Meng, Chen, Chuan-Fa, Sun, Xiao-Fang, Li, Bai-Lian
Format: Article in Journal/Newspaper
Language:unknown
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Online Access:http://www.sciencedirect.com/science/article/pii/S0304380010006356
id ftrepec:oai:RePEc:eee:ecomod:v:222:y:2011:i:14:p:2342-2361
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spelling ftrepec:oai:RePEc:eee:ecomod:v:222:y:2011:i:14:p:2342-2361 2024-04-14T08:20:35+00:00 Surface modelling of global terrestrial ecosystems under three climate change scenarios Yue, Tian-Xiang Fan, Ze-Meng Chen, Chuan-Fa Sun, Xiao-Fang Li, Bai-Lian http://www.sciencedirect.com/science/article/pii/S0304380010006356 unknown http://www.sciencedirect.com/science/article/pii/S0304380010006356 article ftrepec 2024-03-19T10:30:13Z A high accuracy and speed method (HASM) of surface modelling is developed to find a solution for error problem and to improve computation speed. A digital elevation model (DEM) is established on spatial resolution of 13.5km×13.5km. Regression formulations among temperature, elevation and latitude are simulated in terms of data from 2766 weather observation stations scattered over the world by using the 13.5km×13.5km DEM as auxiliary data. Three climate scenarios of HadCM3 are refined from spatial resolution of 405km×270km to 13.5km×13.5km in terms of the regression formulations. HASM is employed to simulate surfaces of mean annual bio-temperature, mean annual precipitation and potential evapotranspiration ratio during the periods from 1961 to 1990 (T1), from 2010 to 2039 (T2), from 2040 to 2069 (T3), and from 2070 to 2099 (T4) on spatial resolution of 13.5km×13.5km. Three scenarios of terrestrial ecosystems on global level are finally developed on the basis of the simulated climate surfaces. The scenarios show that all polar/nival, subpolar/alpine and cold ecosystem types would continuously shrink and all tropical types, except tropical rain forest in scenario A1Fi, would expand because of the climate warming. Especially at least 80% of moist tundra and 22% of nival area might disappear in period T4 comparing with the ones in the period T1. Tropical thorn woodland might increase by more than 97%. Subpolar/alpine moist tundra would be the most sensitive ecosystem type because its area would have the rapidest decreasing rate and its mean center would shift the longest distance towards west. Subpolar/alpine moist tundra might be able to serve as an indicator of climatic change. In general, climate change would lead to a continuous reduction of ecological diversity. Climate; Scenario; Terrestrial ecosystem; HASM; Holdridge life zone; Ecological diversity; Article in Journal/Newspaper Tundra RePEc (Research Papers in Economics)
institution Open Polar
collection RePEc (Research Papers in Economics)
op_collection_id ftrepec
language unknown
description A high accuracy and speed method (HASM) of surface modelling is developed to find a solution for error problem and to improve computation speed. A digital elevation model (DEM) is established on spatial resolution of 13.5km×13.5km. Regression formulations among temperature, elevation and latitude are simulated in terms of data from 2766 weather observation stations scattered over the world by using the 13.5km×13.5km DEM as auxiliary data. Three climate scenarios of HadCM3 are refined from spatial resolution of 405km×270km to 13.5km×13.5km in terms of the regression formulations. HASM is employed to simulate surfaces of mean annual bio-temperature, mean annual precipitation and potential evapotranspiration ratio during the periods from 1961 to 1990 (T1), from 2010 to 2039 (T2), from 2040 to 2069 (T3), and from 2070 to 2099 (T4) on spatial resolution of 13.5km×13.5km. Three scenarios of terrestrial ecosystems on global level are finally developed on the basis of the simulated climate surfaces. The scenarios show that all polar/nival, subpolar/alpine and cold ecosystem types would continuously shrink and all tropical types, except tropical rain forest in scenario A1Fi, would expand because of the climate warming. Especially at least 80% of moist tundra and 22% of nival area might disappear in period T4 comparing with the ones in the period T1. Tropical thorn woodland might increase by more than 97%. Subpolar/alpine moist tundra would be the most sensitive ecosystem type because its area would have the rapidest decreasing rate and its mean center would shift the longest distance towards west. Subpolar/alpine moist tundra might be able to serve as an indicator of climatic change. In general, climate change would lead to a continuous reduction of ecological diversity. Climate; Scenario; Terrestrial ecosystem; HASM; Holdridge life zone; Ecological diversity;
format Article in Journal/Newspaper
author Yue, Tian-Xiang
Fan, Ze-Meng
Chen, Chuan-Fa
Sun, Xiao-Fang
Li, Bai-Lian
spellingShingle Yue, Tian-Xiang
Fan, Ze-Meng
Chen, Chuan-Fa
Sun, Xiao-Fang
Li, Bai-Lian
Surface modelling of global terrestrial ecosystems under three climate change scenarios
author_facet Yue, Tian-Xiang
Fan, Ze-Meng
Chen, Chuan-Fa
Sun, Xiao-Fang
Li, Bai-Lian
author_sort Yue, Tian-Xiang
title Surface modelling of global terrestrial ecosystems under three climate change scenarios
title_short Surface modelling of global terrestrial ecosystems under three climate change scenarios
title_full Surface modelling of global terrestrial ecosystems under three climate change scenarios
title_fullStr Surface modelling of global terrestrial ecosystems under three climate change scenarios
title_full_unstemmed Surface modelling of global terrestrial ecosystems under three climate change scenarios
title_sort surface modelling of global terrestrial ecosystems under three climate change scenarios
url http://www.sciencedirect.com/science/article/pii/S0304380010006356
genre Tundra
genre_facet Tundra
op_relation http://www.sciencedirect.com/science/article/pii/S0304380010006356
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